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Data Science in Tourism and Hospitality

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Tourism, Culture, and Heritage".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 27427

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Special Issue Editors

Prof. Dr. Sérgio Moro

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Guest Editor

Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR-IUL, Lisboa, Portugal
Interests: Data Science; Decision Support; Business Intelligence; Tourism; Hospitality; Marketing

Dr. Ricardo Filipe Ramos

E-Mail Website
Guest Editor

1. Instituto Politécnico de Coimbra, ESTGOH, Oliveira do Hospital, Portugal
2. Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR, Lisboa, Portugal
3. CICEE – Centro de Investigação em Ciências Económicas e Empresariais, Universidade Autónoma de Lisboa, Lisboa, Portugal
Interests: marketing; tourism; consumer behavior
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Special Issue Information

Dear Colleagues,

The world is hungering for data and keeps producing it at an unprecedent rate. Studies within the social sciences are benefiting from such large volumes of data and taking data-driven approaches based on Data Science methods to extract knowledge that leverages decision support. Specifically, tourism and hospitality research is using Data Science to analyze both structured and unstructured data originating from a manifold of sources (Rita et al., 2018).

Given the above context, this Special Issue calls for papers that take Data Science approaches based on tourism and/or hospitality data to increase our knowledge of tourism. Topics of interest for this Special Issue include:

tourism education from a sustainability perspective;
social media analysis in tourism;
data analytics for increased tourism sustainability;
impact of the COVID-19 pandemic on hospitality analyzed through a data science approach;
sustainable hospitality management from a financial performance perspective;
tourism strategy, innovation, and trends;
ecological tourism sustainability;
tourism management: environmental sustainability;
eTourist behavior for hospitality industry sustainability;
tourism circular economy;
tourism green economy;
artificial intelligence and machine learning in tourism;
blockchain for tourism sustainability;
renewable energy for sustainable tourism;
Internet of Things (IoT) for sustainable hospitality;
cultural heritage and tourism sustainability;
data-driven technologies for tourism sustainability;
city break tourism: sustainability for tourist satisfaction;
infrastructure sustainability for sustainable tourism;
transport sustainability from a tourism perspective; and
sustainable demographic tourist visitors.

Nevertheless, the call is broad in its scope and may include many other related themes not mentioned above.

Prof. Dr. Sérgio Moro
Prof. Dr. Ricardo Filipe Ramos
Guest Editors

Reference:

Rita, P., Rita, N., & Oliveira, C. (2018). Data science for hospitality and tourism. Worldwide Hospitality and Tourism Themes, 10(6), 717-725.

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data science
data mining
text mining
big data
data analytics
tourism
hospitality

Published Papers (7 papers)

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Research

13 pages, 857 KiB

Open AccessArticle

Predictors of Hotel Clients’ Satisfaction in the Cape Verde Islands

by Ariana Furtado, Ricardo F. Ramos, Bruno Maia and Joana Martinho Costa

Sustainability 2022, 14(5), 2677; https://0-doi-org.brum.beds.ac.uk/10.3390/su14052677 - 25 Feb 2022

Cited by 12 | Viewed by 2585

Abstract

Tourism has been fundamental for countries’ economic development, and Africa is the destination with the biggest tourism growth potential. Using 1414 travelers’ online reviews collected from TripAdvisor, the present work aims to understand which variables predict the satisfaction of Cape Verde’s hotel clients. Satisfaction was analyzed using sentiment analysis and ANOVA to predict the effect of the gathered variables on clients’ satisfaction. Results indicate that 90% of the clients revealed positive satisfaction and that nationality, date of stay, and previous traveler experiences affect satisfaction. Contrarily to our predictions, there is no statistically significant evidence that gender influences satisfaction. The findings of this study will help hotel marketing managers to align their strategies accordingly and meet their clients’ expectations. Full article

(This article belongs to the Special Issue Data Science in Tourism and Hospitality)

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20 pages, 838 KiB

Open AccessArticle

Public Perception of Tourism Cities before and during the COVID-19 Pandemic through the Lens of User-Generated Content

by Yulin Chen

Sustainability 2021, 13(24), 14046; https://0-doi-org.brum.beds.ac.uk/10.3390/su132414046 - 20 Dec 2021

Cited by 4 | Viewed by 1835

Abstract

The COVID-19 pandemic (coronavirus disease of 2019) sent the world into disarray and devastated the global tourism economy. In 2020 alone, the number of international tourists dropped by roughly 1.1 billion. This study examines user-generated content (UGC) on social media to elucidate the shift in people’s perceptions of popular tourism cities from before the pandemic to during the pandemic. This paper analyzes the characteristics of the cues in tourism-city-related UGC (particularly those related to the pandemic) and identifies the cues that resonate most with the public. This paper collected the data using Instagram’s application programing interface and then sorted the UGC based on content, type, time, likes, share, and comments. Between 1 January 2019 and 31 December 2019, it collected a total of 207,752 pre-pandemic posts and 173,131 peri-pandemic posts. The findings reveal that, during the pandemic, the interactivity of city-related UGC dropped, and only pandemic-related keywords gained public attention. By comparison, pre-pandemic positive posts mentioned local features and contained calls to action that were generally well-received. The findings also validate that UGC effectively reflects and enhances overall public perceptions, suggesting that, in a future which is forced to co-exist with SARS-CoV-2 in the long term, it is important to understand the positive and negative influences of UGC on tourism cities. Full article

(This article belongs to the Special Issue Data Science in Tourism and Hospitality)

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20 pages, 1711 KiB

Open AccessArticle

The Contribution of Online Reviews for Quality Evaluation of Cultural Tourism Offers: The Experience of Italian Museums

by Deborah Agostino, Marco Brambilla, Silvio Pavanetto and Paola Riva

Sustainability 2021, 13(23), 13340; https://0-doi-org.brum.beds.ac.uk/10.3390/su132313340 - 02 Dec 2021

Cited by 10 | Viewed by 3185

Abstract

In the cultural tourism field, there has been an increasing interest in adopting data-driven approaches that are aimed at measuring the service quality dimensions through online reviews. To date, studies measuring quality dimensions in cultural tourism settings through content analysis of online user-generated reviews are mainly based on manual approaches. When the content analysis is automated, these studies do not compare different analytical approaches. Our paper enters this field by comparing two different automated content analysis approaches to evaluate which of the two is more adequate for assessing the quality dimensions through user-generated reviews in an empirical setting of 100 Italian museums. Specifically, we compare a ‘top-down’ content analysis approach that is based on a supervised classification built on policy makers’ guidelines and a ‘bottom-up’ approach that is based on an unsupervised topic model of the online words of reviewers. The resulting museum quality dimensions are compared, showing that the ‘bottom-up’ approach reveals additional quality dimensions compared with those obtained through the ‘top-down’ approach. The misalignment of the results of the ‘top-down’ and ‘bottom-up’ approaches to quality evaluation for museums enhances the critical discussion on the contribution that data analytics can offer to support decision making in cultural tourism. Full article

(This article belongs to the Special Issue Data Science in Tourism and Hospitality)

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21 pages, 1390 KiB

Open AccessArticle

The Role of B Companies in Tourism towards Recovery from the Crisis COVID-19 Inculcating Social Values and Responsible Entrepreneurship in Latin America

by Ángel Acevedo-Duque, Romel Gonzalez-Diaz, Alejandro Vega-Muñoz, Mirtha Mercedes Fernández Mantilla, Luiz Vicente Ovalles-Toledo and Elena Cachicatari-Vargas

Sustainability 2021, 13(14), 7763; https://0-doi-org.brum.beds.ac.uk/10.3390/su13147763 - 12 Jul 2021

Cited by 17 | Viewed by 4248

Abstract

One of the particularities of companies with a social purpose is that, through their business model of B companies, they have incorporated into their processes the necessary mechanisms to obtain, simultaneously, the profits to ensure the existence of the organization in the market. At the same time, social value is generated, which is necessary to address the problems of the social crisis caused by COVID-19 and the environmental problems affecting the community. The current global health and economic crisis has opened up the possibility of adopting business model B and focusing more on the individual. Based on the grounded theory method, we have examined 3500 B Corporations in Latin America, of which 57 were examined in 10 countries listed in the Directory of B Corporations for Latin America. The main conclusions are that B Corporations dedicated to tourism through responsible entrepreneurship develop a more inclusive, sustainable and environmentally friendly economy for the benefit of society, go beyond the notion of CSR and move away from traditional business, as B Corporations combine social development and economic growth. Full article

(This article belongs to the Special Issue Data Science in Tourism and Hospitality)

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18 pages, 3712 KiB

Open AccessArticle

Lifestyle Experiences: Exploring Key Attributes of Lifestyle Hotels Using Instagram User-Created Contents in South Korea

by Yoojin Han and Hyunsoo Lee

Sustainability 2021, 13(5), 2591; https://0-doi-org.brum.beds.ac.uk/10.3390/su13052591 - 01 Mar 2021

Cited by 11 | Viewed by 3651

Abstract

This study aims to investigate the key attributes of a steadily growing hotel sector (lifestyle hotels), which has shown great success in the global competitive market, by analyzing user-created content on Instagram. The dataset used in this study were prepared from a total of 20,999 lifestyle hotel posts and 24,262 boutique hotel posts created from 2013 to 2020 and retrieved using a Python web crawler. The locations, hashtags, and image data were analyzed based on frequency analysis using social network analysis methods and computer vision technology, after which they were visualized with a geographical information system and Gephi. The results demonstrated that lifestyle hotels share key attributes that differentiate them from others in terms of physical, geospatial, and experiential contexts. Design, location, and management type are the main attributes that comprise the distinct identity of each lifestyle hotel. Moreover, a lifestyle hotel is distinct from a boutique hotel in that staying in the former means consuming experiences with continuous changes. The information and knowledge gained from this research will contribute to bridging the gap between theoretical literature and the practical development of lifestyle hospitality. Full article

(This article belongs to the Special Issue Data Science in Tourism and Hospitality)

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24 pages, 7567 KiB

Open AccessArticle

An Evaluation of Green Ryokans through a Tourism Accommodation Survey and Customer-Satisfaction-Related CASBEE–IPA after COVID-19 Pandemic

by Gangwei Cai, Yan Hong, Lei Xu, Weijun Gao, Ka Wang and Xiaoting Chi

Sustainability 2021, 13(1), 145; https://0-doi-org.brum.beds.ac.uk/10.3390/su13010145 - 25 Dec 2020

Cited by 33 | Viewed by 6631

Abstract

Following the outbreak of the COVID-19 pandemic, it became significant to study how to improve the customer satisfaction for Japanese tourist accommodations for restart and recovery in the future, and in preparation for the 2021 Japan Olympics. Therefore, the current paper attempts to evaluate ryokans through descriptive statistics from a tourism accommodation survey and customer-satisfaction-related comprehensive assessment system for built environment efficiency (CASBEE) importance–performance analysis (IPA). Through three progressive studies, three findings were obtained: (1) ryokans are more flexible than hotels, have strong anti-risk capabilities, and have received more and more attention from tourists and support from the Japanese government; (2) improvement strategies for customer satisfaction after COVID-19 were provided from IPA; and (3) a dynamic evaluation model of green ryokans was discussed and may be employed in other countries and regions experiencing the same situation. Full article

(This article belongs to the Special Issue Data Science in Tourism and Hospitality)

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19 pages, 3145 KiB

Open AccessArticle

Exploring User-Generated Content for Improving Destination Knowledge: The Case of Two World Heritage Cities

by Nuno Antonio, Marisol B. Correia and Filipa Perdigão Ribeiro

Sustainability 2020, 12(22), 9654; https://0-doi-org.brum.beds.ac.uk/10.3390/su12229654 - 19 Nov 2020

Cited by 13 | Viewed by 2786

Abstract

This study explores two World Heritage Sites (WHS) as tourism destinations by applying several uncommon techniques in these settings: Smart Tourism Analytics, namely Text mining, Sentiment Analysis, and Market Basket Analysis, to highlight patterns according to attraction, nationality, and repeated visits. Salamanca (Spain) and Coimbra (Portugal) are analyzed and compared based on 8,638 online travel reviews (OTR), from TripAdvisor (2017–2018). Findings show that WHS reputation does not seem to be relevant to visitors-reviewers. Additionally, keyword extraction reveals that the reviews do not differ from language to language or from city to city, and it was also possible to identify several keywords related to history and heritage; in particular, architectural styles, names of kings, and places. The study identifies topics that could be used by destination management organizations to promote these cities, highlights the advantages of applying a data science approach, and confirms the rich information value of OTRs as a tool to (re)position the destination according to smart tourism design tenets. Full article

(This article belongs to the Special Issue Data Science in Tourism and Hospitality)

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